Software architecture tool demonstrations

In this paper, we describe the short summary of the tool demonstrations session at WICSA/ECSA 2012. The session aimed to attract both tools in practice and research tools. We describe the targeted topics for the tool support, and report on the program. Copyright is held by author/owner(s)

A web-based infrastructure for recording user demonstrations of mobile manipulation tasks

Abstract — Learning from demonstration (LfD) is a common technique applied to many problems in robotics, such as populating grasp databases, training for reinforcement learning of high-level skill sets and bootstrapping motion planners. While such approaches are generally highly valued, they rely on the often time-consuming process of gathering user demonstrations, and hence it becomes difficult to attain a sizeable dataset. In this paper, we present a tool capable of recording large numbers of high-dimensional demonstrations of mobile manipulation tasks provided by non-experts in the field. Our tool accomplishes this via a web interface that requires no additional software to be in-stalled beyond a web browser, as well as a scalable architecture that is capable of supporting 10 concurrent demonstrators on a single server. Our architecture employs a lightweight simulation environment to reduce unnecessary computations and improve performance. Furthermore, we show how our tool can be used to gather a large set of demonstrations of a mobile manipulation task by leveraging existing crowdsource platforms. The data set collected has been made available to the robotics community. We also present experiments in which we apply demonstrations collected through our infrastructure to teach a robot how to grasp, to teach a robot how to perform dexterous manipulation tasks such as scooping and to accelerate motion planning for full-body manipulation tasks. I

Space-centred information management approach to improve CAD-based healthcare building design

This study focuses on developing a space-centred CAD tool to enable designers to effectively manage and implement the information of design guidance information and user requirements during design processes, especially for the stages of design briefing and conceptual design. It aims to structure and store design guidance and user requirements for healthcare building design into a relational database, and link them to relevant space entities in design plans. The tool is developed on the platform of Autodesk Architecture Desktop (ADT). It also enables users to store and retrieve pictures associated with textual information, because pictures have been always used by designers as an effective medium to represent and deliver design information and knowledge. This can give users directly visual and more understandable perceptions of the design guidance. The tool is fully embedded with Autodesk AutoCAD systems to ensure the application of this tool being fully merged with CAD-based design process. A set of design guidance about Alzheimer clinic built environments are adopted as a sample to demonstrate and validate the tool. Moreover, the scenario of expanding this application to more broad areas has also been foreseen

The NASA/OAST telerobot testbed architecture

Through a phased development such as a laboratory-based research testbed, the NASA/OAST Telerobot Testbed provides an environment for system test and demonstration of the technology which will usefully complement, significantly enhance, or even replace manned space activities. By integrating advanced sensing, robotic manipulation and intelligent control under human-interactive supervision, the Testbed will ultimately demonstrate execution of a variety of generic tasks suggestive of space assembly, maintenance, repair, and telescience. The Testbed system features a hierarchical layered control structure compatible with the incorporation of evolving technologies as they become available. The Testbed system is physically implemented in a computing architecture which allows for ease of integration of these technologies while preserving the flexibility for test of a variety of man-machine modes. The development currently in progress on the functional and implementation architectures of the NASA/OAST Testbed and capabilities planned for the coming years are presented

DeepMutation: A Neural Mutation Tool

Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants. To address the first point, we recently proposed an approach using a Recurrent Neural Network Encoder-Decoder architecture to learn mutants from ~787k faults mined from real programs. The empirical evaluation of this approach confirmed its ability to generate mutants representative of real faults. In this paper, we address the second point, presenting DeepMutation, a tool wrapping our deep learning model into a fully automated tool chain able to generate, inject, and test mutants learned from real faults. Video: https://sites.google.com/view/learning-mutation/deepmutationComment: Accepted to the 42nd ACM/IEEE International Conference on Software Engineering (ICSE 2020), Demonstrations Track - Seoul, South Korea, May 23-29, 2020, 4 page